Results 21 to 30 of about 1,979,450 (271)
Deep Transfer Learning for Biology Cross-Domain Image Classification
Journal of Control Science and Engineering, 2021 Automatic biology image classification is essential for biodiversity conservation and ecological study. Recently, due to the record-shattering performance, deep convolutional neural networks (DCNNs) have been used more often in biology image ...
Chunfeng Guo, Bin Wei, Kun Yu
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Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
Applied Sciences, 2020 Transfer learning algorithms have been widely studied for machine learning in recent times. In particular, in image recognition and classification tasks, transfer learning has shown significant benefits, and is getting plenty of attention in the research
Arjun Magotra, Juntae Kim
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On spatial selectivity and prediction across conditions with fMRI [PDF]
, 2012 Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs).
Schwartz, Yannick +2 more
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Frontiers in Earth Science, 2023 The main purpose of this study is to analyze the main influencing factors of the landslide in the coal mine area and, on this basis, establish the sensitivity zoning model of the landslide.
Yongguo Zhang +3 more
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Stochastic Ensemble Policy Transfer [PDF]
Jisuanji kexue yu tansuo, 2022 Reinforcement learning (RL) has achieved great success on sequential decision-making problems. Along with the fast advances of RL, transfer learning (TL) arises as an important technique to accelerate the learning process of RL by leveraging and ...
CHANG Tian, ZHANG Zongzhang, YU Yang
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Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
, 2020 Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or
Cheng, Yingfeng +10 more
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Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019 In machine learning, transfer learning is concerned with utilising prior knowledge as a way to improve the process of training a new model in a different, but related, domain. Transfer learning has been shown to be beneficial across a large set of problems.
Brandon Muller +3 more
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Exploring Metacognition as Support for Learning Transfer
Teaching & Learning Inquiry: The ISSOTL Journal, 2017 The ability to transfer learning to new situations lies at the heart of lifelong learning and the employability of university graduates. Because students are often unaware of the importance of learning transfer and staff do not always explicitly ...
Lauren Scharff +6 more
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Constrained Deep Transfer Feature Learning and its Applications
, 2017 Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however, typically requires a large amount of training data, which may not be feasible for ...
Ji, Qiang, Wu, Yue
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Transfer Learning for Speech and Language Processing [PDF]
, 2015 Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language ...
Wang, Dong, Zheng, Thomas Fang
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